Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add arpitexplores/skills-super --skill super-data-analyticsgit clone --depth 1 https://github.com/arpitexplores/skills-superWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/arpitexplores/skills-super/super-data-analytics)<a href="https://agentmods.dev/skills/arpitexplores/skills-super/super-data-analytics"><img src="https://agentmods.dev/badge/skills/arpitexplores/skills-super/super-data-analytics.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00022 | $0.00356 |
| Opus 5 | $0.00011 | $0.00178 |
| Sonnet 5 | $0.00004 | $0.00071 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
super-data-analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Super Data & Analytics
Overview
Build reliable data pipelines and analytics outputs with measurable insights.
User Intent Examples
- "Need help with Product Analytics for my product/site."
- "Create a plan for Data Engineering."
- "Audit or improve Data Science."
Workflow
- Define business questions, metrics, and data sources.
- Design ingestion and transformation pipelines.
- Select storage, modelling, and access patterns.
- Implement analytics, dashboards, and reporting.
- Validate data quality and performance.
- Document lineage, ownership, and SLAs.
Minimal Intake Questions
- Primary goal or outcome
- Scope (pages, systems, teams, or timeframe)
- Constraints (tools, budget, timeline)
Output Format
- Data pipeline plan
- Data model and storage choices
- Analytics and dashboard spec
- Data quality checklist
- Operations and SLA notes
Routing Map (Modules)
- Product Analytics ->
references/modules/analytics-product.md - Data Engineering ->
references/modules/data-engineer.md - Data Science ->
references/modules/data-scientist.md
Bundled References
references/modules/scripts/assets/agents/
Compatibility Notes
- If any module references slash commands or tool-specific paths, translate them into plain-language steps.
- Keep outputs platform-agnostic unless the user specifies a specific tool, stack, or agent.
Guardrails
- Do not report metrics without validation.
- Separate raw data from transformed outputs.
- Track lineage and ownership explicitly.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago First seen · 56 lines · 22 tokens per session scan A 757cba9c85e4
super-data-analytics is a skill published in the GitHub repository arpitexplores/skills-super (2 stars, last pushed 4mo ago), licensed MIT. It adds 22 tokens to every session and 356 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
swarm-pr-review
Run a graph-guided, tool-augmented PR review using context packing, parallel exploration, mandatory repository-agnostic risk-family coverage with dispatch scaled to diff size and risk, independent reviewer validation, critic challenge, and metrics writeback. Use for deep pull request review with low false-positive…
safe-extraction
Apply when extracting code from a large monolith file into submodules. Covers barrel re-exports, internals DI seam proxy patterns, CI invariant allowlist updates, and cross-file test verification. Prevents CI failures, broken imports, and test regressions from code extraction.
bundle-safety
Bundle transform safety — minification variant selection, consumer-constraint verification, identifier preservation, and namespace re-export coverage for build output.
durable-session-state
Persist plans, scope decisions, evidence, and reviewer/critic verdicts to durable files during long or multi-phase tasks so work survives context compaction, session resumes, and handoffs. Use for swarm-mode tasks, before context grows large, when recording approval gates, and when resuming after compaction or a…
confirm-failures-are-causally-linked-to-the-task-before-reportin
When delegating a task affected by this skill, include.
include-test-files-that-assert-on-behavior-being-changed-in-decl
When delegating a task affected by this skill, include.